Understanding GPT Models and Fine-Tuning for Specific Domains

The video tutorial introduces GPT models, highlighting their capabilities in natural language processing. It discusses their limitations, such as quality constraints, prompt size, cost, and latency. The tutorial then explains fine-tuning, a technique to adapt pre-trained models for specific tasks, enhancing their performance in particular domains.

What does prompt engineering involve when working with GPT models?

Discuss the cost implications of using GPT models for tokenization.